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Core Imaging Facility (CIF)
In close cooperation with the Neuroimaging Centre (NIC) at the Technische Universität Dresden, the Core Imaging Facility (CIF) of the collaborative research center (CRC) provides a shared resource for all CRC projects with respect to imaging physics and advanced image analysis methodologies for functional magnetic resonance imaging (fMRI) data.
In the past funding periods, the Core Imaging Facility has contributed to the CRC by providing advanced imaging protocols, image analysis tools, support with experimental designs, advanced methods with respect to task-based functional connectivity, multivariate analyses of functional activity and connectivity measures, and computational models of behavioural and neuroimaging data. Specifically, new imaging protocols for functional, multiband echo planar imaging (EPI) und quantitative structural MRI have been applied. Diffusion tensor imaging (DTI) has been employed in acute anorexia nervosa and changes in resting state functional connectivity have been accessed including alterations induced by repetitive transcranial magnetic stimulation. The NICePype workflow engine for automated and parallelized processing of imaging data has been extended to include semi-automatic input configuration, quality control, and processing pipelines. In addition, we have established various advanced analysis tools for large-scale analyses of brain activity and brain connectivity, multivariate analysis of functional connectivity patterns, and time-resolved multivariate analysis of representational activity dynamics. Furthermore, we have supported CRC members in using advanced computational models for both behavioural and neuroimaging data, and derived novel update equations for the active inference framework. We also continued our teaching program for experimental scientists with respect to programming in Python and Matlab, utilization of data management resources, advanced fMRI design and analysis methods and the use of computational models in cognitive neuroscience.
Project Members
Principal Investigators
Prof. Dr. Stefan Kiebel
Professor of Neuroimaging
Phone: +49 (0)351 - 431 45
E-Mail:
Ph.D. Michael Marxen
Group Leader Brain Dynamics and Imaging Methods
Phone: +49 (0)351 463-42212
E-Mail: michael.marxen@tu-dresden.de
Prof. Dr. rer. nat. Hannes Ruge
Head of the research group Neuroimaging of Higher Cognitive Brain Function
Phone: +49 (0)351 463-33824
E-Mail:
Staff
M.Sc. Xiaoyu Wang PhD candidate Telephone: +49 (0)351 463 37486 Wang, Xiaoyu <>
MSc. Ben Wagner PhD candidate Telephone: +49(0)351 463 42695
MSc. Sarah Schwöbel
PhD candidate
Phone: +49 (0)351 463-42697
E-Mail:
MSc. Marco Bottino
PhD candidate
Phone: +49 (0)351 463-42208
E-Mail:
Publications
Böhmer, J., Reinhardt, P., Garbusow, M., Marxen, M., Smolka, M.N., Zimmermann, U.S., Heinz, A., Bzdok, D., Friedel, E., Kruschwitz, J.D., Walter, H. (2023) Aberrant functional brain network organization is associated with relapse during 1-year follow-up in alcohol-dependent patients. Addict Biol. (11):e13339. doi: 10.1111/adb.13339. PubMed PMID: 37855075.
Chen, H.Y., Marxen, M., Dahl, M.J., Glöckner, F (2023). Effects of Adult Age and Functioning of the Locus Coeruleus Norepinephrinergic System on Reward-Based Learning. J Neurosci. 43(35):6185-6196. doi: 10.1523/JNEUROSCI.2006-22.2023
Marxen, M., Graff, J.E., Riedel, P., Smolka, M.N. (2023) Observing cognitive processes in time through functional MRI model comparison. Hum Brain Mapp. 2023 Mar;44(4):1359-1370. doi: 10.1002/hbm.26114
Geisler, D., King, J.A., Bahnsen, K., Bernardoni, F., Doose, A., Müller, D.K., Marxen, M., Roessner, V., van den Heuvel, M., Ehrlich, S. Altered White Matter Connectivity in Young Acutely Underweight Patients With Anorexia Nervosa. J Am Acad Child Adolesc Psychiatry. 2022 Feb;61(2):331-340. doi: 10.1016/j.jaac.2021.04.019
Marxen, M., Jacob, M. J., Hellrung, L., Riedel, P., & Smolka, M. N. (2021). Questioning the role of amygdala and insula in an attentional capture by emotional stimuli task. Hum Brain Mapp, 42(5), 1257-1267. doi:10.1002/hbm.25290
Haugg, A., Renz, F. M., Nicholson, A. A., Lor, C., Gotzendorfer, S. J., Sladky, R., . . . Steyrl, D. (2021). Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis. Neuroimage, 237, 118207. doi:10.1016/j.neuroimage.2021.118207
Geisler, D., King, J. A., Bahnsen, K., Bernardoni, F., Doose, A., Muller, D. K., . . . Ehrlich, S. (2021). Altered White Matter Connectivity in Young Acutely Underweight Patients With Anorexia Nervosa. J Am Acad Child Adolesc Psychiatry. doi:10.1016/j.jaac.2021.04.019
Fang, X., Deza-Araujo, Y. I., Petzold, J., Spreer, M., Riedel, P., Marxen, M., . . . Smolka, M. N. (2021). Effects of moderate alcohol levels on default mode network connectivity in heavy drinkers. Alcoholism-Clinical and Experimental Research, 45(5), 1039-1050. doi:10.1111/acer.14602
Schwöbel, S., Markovic, D., Smolka, M. N., & Kiebel, S. J. (2021). Balancing control: A Bayesian interpretation of habitual and goal-directed behavior. Journal of Mathematical Psychology, 100. doi:10.1016/j.jmp.2020.
Mohr, H., & Ruge, H. (2021). Fast Estimation of L1-Regularized Linear Models in the Mass-Univariate Setting. Neuroinformatics, 19(3), 385-392. doi:10.1007/s12021-020-09489-1
Markovic, D., Goschke, T., & Kiebel, S. J. (2021). Meta-control of the exploration-exploitation dilemma emerges from probabilistic inference over a hierarchy of time scales. Cognitive, Affective & Behavioral Neuroscience, 21(3), 509-533. doi:10.3758/s13415-020-00837-x
Muller, E. J., Munn, B., Mohr, H., Ruge, H., & Shine, J. M. (2021). Brain state kinematics and the trajectory of task performance improvement. Neuroimage, 243, 118510. doi:10.1016/j.neuroimage.2021.118510
Sheffield, J. M., Mohr, H., Ruge, H., & Barch, D. M. (2021). Disrupted Salience and Cingulo-Opercular Network Connectivity During Impaired Rapid Instructed Task Learning in Schizophrenia. Clinical Psychological Science, 9(2), 210-221. doi:Artn 2167702620959341
Kronke, K. M., Mohr, H., Wolff, M., Kraplin, A., Smolka, M. N., Buhringer, G., . . . Goschke, T. (2021). Real-Life Self-Control is Predicted by Parietal Activity During Preference Decision Making: A Brain Decoding Analysis. Cognitive, Affective and Behavioral Neuroscience, 21(5), 936-947. doi:10.3758/s13415-021-00913-w
Geisler, D., Borchardt, V., Boehm, I., King, J. A., Tam, F. I., Marxen, M., . . . Ehrlich, S. (2020). Altered global brain network topology as a trait marker in patients with anorexia nervosa. Psychol Med, 50(1), 107-115. doi:10.1017/S0033291718004002
Knorr, F. G., Neukam, P. T., Frohner, J. H., Mohr, H., Smolka, M. N., & Marxen, M. (2020). A comparison of fMRI and behavioral models for predicting inter-temporal choices. Neuroimage, 211, 116634. doi:10.1016/j.neuroimage.2020.116634
Deza-Araujo, Y. I., Neukam, P. T., Marxen, M., Muller, D. K., Henle, T., & Smolka, M. N. (2019). Acute tryptophan loading decreases functional connectivity between the default mode network and emotion-related brain regions. Hum Brain Mapp, 40(6), 1844-1855. doi:10.1002/hbm.24494
Knorr, F. G., Petzold, J., & Marxen, M. (2019). PyParadigm-A Python Library to Build Screens in a Declarative Way. Front Neuroinform, 13, 59. doi:10.3389/fninf.2019.00059
Mohr, H., Cichy, R. M., & Ruge, H. (2019). Deep neural networks can predict human behavior in arcade games. 2019 Conference on Cognitive Computational Neuroscience. doi:10.32470/CCN.2019.1043-0
Riedel, P., Heil, M., Bender, S., Dippel, G., Korb, F. M., Smolka, M. N., & Marxen, M. (2019). Modulating functional connectivity between medial frontopolar cortex and amygdala by inhibitory and excitatory transcranial magnetic stimulation. Hum Brain Mapp, 40(15), 4301-4315. doi:10.1002/hbm.24703
Ruge, H., Schafer, T. A., Zwosta, K., Mohr, H., & Wolfensteller, U. (2019). Neural representation of newly instructed rule identities during early implementation trials. Elife, 8, e48293. doi:10.7554/eLife.48293
Fechtelpeter, J., Ruge, H., & Mohr, H. (2019). The cingulo-opercular network controls stimulus- response transformations with increasing efficiency over the course of learning. 2019 Conference on Cognitive Computational Neuroscience. doi:10.32470/CCN.2019.1060-0
von Schwanenflug, N., Muller, D. K., King, J. A., Ritschel, F., Bernardoni, F., Mohammadi, S., . . . Ehrlich, S. (2019). Dynamic changes in white matter microstructure in anorexia nervosa: findings from a longitudinal study. Psychol Med, 49(9), 1555-1564. doi:10.1017/S003329171800212X
Mohr, H., Wolfensteller, U., & Ruge, H. (2018). Large-scale coupling dynamics of instructed reversal learning. Neuroimage, 167, 237-246. doi:10.1016/j.neuroimage.2017.11.049
Schwöbel, S.; Kiebel, S.; Marković, D. [2018]. Active Inference, Belief Propagation, and the Bethe Approximation. Neural Computation. Doi.
Mohr, H., Zwosta, K., Markovic, D., Bitzer, S., Wolfensteller, U., & Ruge, H. (2018). Deterministic response strategies in a trial-and-error learning task. PLoS Computational Biology, 14(11), e1006621. doi:10.1371/journal.pcbi.1006621
Ruge, H., Legler, E., Schäfer, T. A. J., Zwosta, K., Wolfensteller, U., & Mohr, H. (2018). Unbiased Analysis of Item-Specific Multi-Voxel Activation Patterns Across Learning. Frontiers in Neuroscience, 12, 723. doi:10.3389/fnins.2018.00723
Marxen, M., Jacob, M. J., Muller, D. K., Posse, S., Ackley, E., Hellrung, L., . . . Smolka, M. N. (2016). Amygdala Regulation Following fMRI-Neurofeedback without Instructed Strategies. Front Hum Neurosci, 10, 183. doi:10.3389/fnhum.2016.00183
Mohr, H., Wolfensteller, U., Betzel, R. F., Misic, B., Sporns, O., Richiardi, J., & Ruge, H. (2016). Integration and segregation of large-scale brain networks during short-term task automatization. Nature Communications, 7, 13217. doi:10.1038/ncomms13217
King, J. A., Geisler, D., Ritschel, F., Boehm, I., Seidel, M., Roschinski, B., . . . Ehrlich, S. (2015). Global cortical thinning in acute anorexia nervosa normalizes following long-term weight restoration. Biol Psychiatry, 77(7), 624-632. doi:10.1016/j.biopsych.2014.09.005
Mohr, H., Wolfensteller, U., Frimmel, S., & Ruge, H. (2015). Sparse regularization techniques provide novel insights into outcome integration processes. Neuroimage, 104(0), 163-176. doi:10.1016/j.neuroimage.2014.10.025·
Riedel, P., Jacob, M. J., Muller, D. K., Vetter, N. C., Smolka, M. N., & Marxen, M. (2016). Amygdala fMRI Signal as a Predictor of Reaction Time. Front Hum Neurosci, 10, 516. doi:10.3389/fnhum.2016.00516
Gan, G., Guevara, A., Marxen, M., Neumann, M., Jünger, E., Kobiella, A., Mennigen, E., Pilhatsch, M., Schwarz, D., Zimmermann, U.S., & Smolka, M.N. (2014) Alcohol-Induced Impairment of Inhibitory Control Is Linked to Attenuated Brain Responses in Right Fronto-Temporal Cortex, Biological Psychiatry, 76(9), 698-707. doi:10.1016/j.biopsych.2013.12.017
Marxen, M., Gan, G., Schwarz, D., Mennigen, E., Pilhatsch, M., Zimmermann, U.S., Guenther, M., & Smolka, M.N. (2014) Acute effects of alcohol on brain perfusion monitored with arterial spin labeling magnetic resonance imaging in young adults. Journal of Cerebral Blood Flow & Metabolism, 34(3), 472-479. doi:10.1038/jcbfm.2013.223